Call for Papers

Recent years have witnessed explosive growth in using Supervised, Un-Supervised, Semi-supervised, Reinforcement, and Deep Reinforcement Learning to solve networking issues. Nevertheless, since next-generation networks are complex, dynamic, and non-centralized by nature, it is worth exploring Federated Learning and its variations to cognitive network management set as making automated decisions for management actions through autonomous, zero-touch, self-driven, and knowledge-driven techniques. Federated Learning is a particular distributed machine learning approach. Distributed machine learning algorithms create accurate models using multiple servers, usually containing datasets of around the same size with independent and identically distributed samples, aiming to improve the learning process regarding time, memory, and bandwidth. Federated Learning algorithms hold the potential of becoming one of the leading 6G enablers since they can build accurate models from vast decentralized and heterogeneous datasets on resource-constrained devices (e.g., gateways, edge devices, smartphones, and autonomous vehicles). Furthermore, the Federated Learning process can be coordinated by centralized nodes (e.g., 5G/6G network data analytics functions and SDN controllers) or collaboratively by distributed nodes (e.g., in-slice managers and programmable switches). Federated Deep Reinforcement Learning and Online Federated Learning have been proposed recently for leveraging the potential of combining Federated Learning, Deep Reinforcement Learning, and Online Learning. In these combinations, learning agents would learn deeply and continuously by interacting with the environment to meet eXperience Level Agreements and Service Level Agreements in 5G, 6G, and datacenter networks, for instance.

This Feature Topic (FT) aims to bring together researchers, industry practitioners, and individuals working on the related areas to share their new ideas, latest findings, and state-of- the-art results. Prospective authors are invited to submit articles on topics including, but not limited to:

• Federated Learning for Security Management
• Federated Learning for Fault Management
• Federated Learning for Configuration Management
• Federated Learning for Performance Management
• Federated Learning for  Accounting Management
• Cognitive Management of 5G/6G Networks based on Federated Learning
• Federated Learning tools and data-driven design for 5G/6G networks
• Blockchain and Federated Learning for 6G Networking
• Federated Learning for Managing SDN/NFV-based networks
• Deep Federated Learning for network management functional areas
• Federated Reinforcement Learning  for network management tasks
• Federated Deep Reinforcement Learning for network management functional areas
• Explainable Federated Learning for supporting network management decisions
• Online Federated Learning for realizing the functional network management areas
• Streaming Federated Learning for the functional network management areas
• Federated Learning for Zero-Touch Management and Autonomous Management
• eXplainable FL for Zero-Touch Management  and Autonomous Management
• Resource Management based on Federated Learning for 5G and beyond networks
• Micro-services for network management based on Federated Learning
• Federated Learning for programmable data planes
• Experimental demonstrations and prototypes

Submission Guidelines

Manuscripts should conform to the standard format as indicated in the Information for Authors section of the Manuscript Submission Guidelines. Please, check these guidelines carefully before submitting since submissions not complying with them will be administratively rejected without review.

All manuscripts to be considered for publication must be submitted by the deadline through Manuscript Central. Select the "FT-2209/Federated Learning for Cognitive Networks" topic from the drop-down menu of Topic/Series titles. Please observe the dates specified here below noting that there will be no extension of submission deadline.

Important Dates

Manuscript Submissions Deadline: 1 ​August 2022
Decision Notification: ​15 December 2022
Final Manuscript Due: 1 January 2023
Publication Date: ​March 2023

Guest Editors

Oscar Mauricio Caicedo Rendon
Universidad del Cauca, Colombia

Filip De Turck
Ghent University, Belgium

Chadi Assi
Concordia University, Canada

Riccardo Trivisonno
Huawei Technologies, Germany